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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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Impact of Online Education Learning During Covid 19 on Children's Academic Performance

Impact of Online Education Learning During Covid 19 on Child...

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computing and data Science (ICCDS), international conference on

作者： Shruti Mishra Shruti Srivastava Vaishnavi Kharote Aman Pushp Rahul Singh Gautam Shailesh Rastogi Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The COVID-19 pandemic has led to a broad trend toward online learning, drastically changing the way kids learn in traditional classroom settings. This study looks on how children's academic performance was affected by online education during the COVID-19 pandemic. This research intends to clarify the effects of online education on many elements of children's academic development, such as cognitive skills, knowledge retention, social interaction, and emotional well-being, by a thorough analysis of the body of existing literature and empirical data. The study investigates variables including technological availability, parental involvement, teacher support, and educational resources that affect how effective online learning platforms are. Furthermore, potential differences in educational outcomes between various demographic groups are investigated, taking into account learning difficulties, socioeconomic position, and access to the internet. This research helps to understand the complex effects of online learning on kids' academic performance in the midst of the COVID-19 pandemic by synthesizing empirical data and theoretical frameworks. This knowledge helps parents, educators, and policymakers create effective plans to support kids' learning and development in the digital age.

关键词： COVID-19 Pandemics Distance learning Education data science Market research Information age

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High-Performance FFT Code Generation via MLIR Linalg Dialect and SIMD Micro-Kernels

High-Performance FFT Code Generation via MLIR Linalg Dialect...

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ieee international conference on Cluster computing

作者： Yifei He Stefano Markidis KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

Fast Fourier Transform (FFT) libraries are an indispensable and critical component of any High-Performance computing (HPC) software stack. They are used in many applications, from Partial Differential Equation (PDE) solvers to signal spectral analysis and deep-learning. To design and develop the next generation of HPC FFT libraries, it is essential to leverage modern compiler infrastructures, such as Multi-Level Intermediate Representation (MLIR) and the Low-Level Virtual Machine (LLVM), alongside advanced computer architecture features, including SIMD (Single Instruction, Multiple data) instructions. In this work, we introduce FFTc 2.0, an MLIR-based domain-specific compilation framework for FFT. We extend the MLIR Linalg dialect with FFT-specific operations to harness high-level tensor-based abstractions for FFT. These abstractions are ideal for formulating various FFT algorithms and facilitating formula rewriting for FFT decomposition and cache-friendly optimization. We employ micro-kernels to increase the performance, particularly in response to the limited support for complex arithmetic in MLIR and the general-purpose compiler LLVM. These micro-kernels are designed to implement small-size FFT kernels for integration into larger FFTs. They utilize SIMD-friendly data layouts for complex arithmetic and feature an optimized memory access pattern, enabling performance enhancements that are not achievable with standard compiler implementations. Our method achieves performance levels comparable to or surpass those of widely used FFT HPC libraries, such as FFTW, FFTE, and Spiral FFT. Additionally, it establishes a robust software infrastructure that facilitates further optimizations and supports additional hardware backends.

关键词： Codes Fast Fourier transforms Single instruction multiple data High performance computing Layout Full stack Libraries Optimization Arithmetic Python

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Fededrated Learning: Research, Challenges, and Future Directions

Fededrated Learning: Research, Challenges, and Future Direct...

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computing, Power and Communication Technologies (IC2PCT), ieee international conference on

作者： Kuldeep Singh Kaswan Jagjit Singh Dhatterwal Rishika Yadav Kiran Malik Aanjey Mani Tripathi School of Computing Science and Engineering Galgotias University Greater Noida UP India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Engineering Graphic Era Hills University Dehradun Uttarakhand India Department of Computer Science and Engineering MRIEM Rohtak India School of Computing Science and Engineering Galgotias University Greater Noida India

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field’s increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning’s full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms.

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Research on Image Encryption Method Based on complex Analytical Chaotic Map

Research on Image Encryption Method Based on Complex Analyti...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Yanling Sun Yingqi Li Department of Science Shenyang Jianzhu University Shenyang China Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Targeting the security issues associated with digital image transmission across networks, a digital image encryption technique based on Piecewise linear chaotic map (PWLCM) and complex analytical chaotic map (ICON) was presented. Firstly, ICON was introduced to overcome the problem that the traditional chaotic map is too simple and the generated random sequence is too single. The HMAC-SHA256 hash algorithm is used to calculate the plaintext image and generate the key stream required for encryption, which improves the plaintext sensitivity of the algorithm. The algorithm employs a scrambling-diffusion framework. During the scrambling phase, it integrates overall scrambling and separate scrambling across three channels to significantly diminish pixel correlations. In the diffusion phase, it utilizes XOR operations alongside S-box replacement diffusion to extensively spread pixel values. Experimental findings indicate that the encryption method exhibits robust key sensitivity, minimal correlation among neighboring pixels in encrypted images, and adeptness in countering differential attacks. Even if the encrypted data is missing, most of the information can still be obtained after decryption, which shows that the scheme has high security and reliability.

关键词： Sensitivity Correlation Digital images Resists Streaming media data transfer Encryption Reliability S Box Random sequences

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Poster: Selection of Optimal Neural Model using Spiking Neural Network for Edge computing*

Poster: Selection of Optimal Neural Model using Spiking Neur...

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international conference on Distributed computing Systems

作者： Sanaullah Kaushik Roy Ulrich Ruckert Thorsten Jungeblut Dep. of Eng. and Math. Industrial IOT University of Applied Sciences and Arts Bielefeld Germany Dep. of Computer Science Cyber Defense and AI Lab Lab North Carolina A&T State University NC USA Dep. of Faculty of Technology Cognitive Interaction Technology Universitat Bielefeld Bielefeld Germany

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Spiking Neural Networks (SNNs), are inspired by the biological brain's complicated signaling mechanisms and possess unique characteristics that set them apart from traditional artificial neural networks. This research study explores the challenging domain of image classification, specifically utilizing the well-known MNIST dataset through the development and thorough evaluation of different neural models for edge computing. However, the primary contribution is the autonomous selection of the best-performing SNN model through various early stopping approaches and validation functions, allowing the models to autonomously adapt during training. In addition, this article presents the standalone AutoML-SNN model, which is the introduction of dynamic elements into selected SNN domains, enhancing their adaptability to complex patterns within the dataset. Furthermore, the early stopping methodologies are used to reduce overfitting hazards, and using the 3000-neuron set, the LIF appeared as the most proficient neural model.

关键词： Training Adaptation models Computational modeling Biological system modeling Neurons Spiking neural networks Hazards

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Cloud Technology Impact on Human Behavior Based on cognitive Theory

Cloud Technology Impact on Human Behavior Based on Cognitive...

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Electrical, Computer and Energy Technologies (ICECET), international conference on

作者： Drajad Wiryawan Basil Rattanto Muhammad Shabir Shaharudin Joni Suhartono Adele Bernadet Lingkan Mailangkay Stefanus Rumangkit School of Information Systems Bina Nusantara University Jakarta Indonesia School of Management Universiti Sains Malaysia Penang Malaysia School of Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

The relationship between cognitive Theory and Cloud Technology is a learning stage for humans, especially technology users, in various innovations used by users in modeling reasoning to develop human direction toward a better future. It includes learning on a large scale, such as objectively coordinated thinking, which provides for decision-making, social interaction, and access to various technological devices used. The above also focuses on human familiarity with various distributed computing services in cloud technology, including PaaS, SaaS, and IaaS. This research used a systems literature review model for over 20 years (2000 – 2023). Meanwhile, the applications used are Vos Viewer and Publish or Perish. Due to the author's limited access to ieee, ACM, and WoS, the database used and the focus of the literature is the Scopus database. In five years (2019–2023), the writing purpose accounted for only 55 articles, with 39 explicitly addressing cognitive theory and human behavior in the context of cloud technology. In this research, five factors influence human behavior with cloud technology: social interaction, access and connectivity, problem-solving, decision-making, and data confidentiality security. However, of the five impacts entered and obtained from the papers and journals studied, the most critical findings from this research are social interaction, data privacy and security, and decision-making. People will use cloud technology for social interaction, to protect and maintain the confidentiality of their data, and to make various decisions related to their activities. This research also shows that most references come from journals, not conference proceedings articles.

关键词： Cloud computing data privacy Technological innovation databases Computational modeling Bibliographies Decision making Behavioral sciences Security Problem-solving

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Backdoor Attacks Optimized through Genetic Algorithm-Driven data Augmentation Combinations in Deep Neural Networks

Backdoor Attacks Optimized through Genetic Algorithm-Driven ...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yilun Lyu Xu Ma Yuan Ma Qufu Normal University Qufu China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Deep neural networks (DNNs) have become increasingly popular across a wide range of applications, but recent research has revealed their vulnerability to backdoor attacks. However, existing backdoor attack methods often involve complex computational costs, making them less practical in real-world scenarios. data augmentation, a commonly used technique for improving model generalization, offers an ideal source for trigger generation due to its low computational overhead and the fact that the generated images do not raise suspicion. However, current backdoor attack strategies that employ data augmentation typically rely on randomly selected augmentation techniques, which yield suboptimal results. In this paper, we propose BGD3A, a novel backdoor attack method driven by a genetic algorithm, optimizing the selection of data augmentation combinations. Attackers can customize the fitness function to generate an optimal combination of augmentation techniques to meet their objectives. Extensive experimental results demonstrate that BGD3A not only achieves high attack success rates but also remains robust against state-of-the-art backdoor defense mechanisms.

关键词： data privacy Computational modeling Artificial neural networks Manuals Inspection data augmentation Robustness data models Security Genetic algorithms

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Research on Information Security and Privacy Protection Model for Big data

Research on Information Security and Privacy Protection Mode...

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ieee international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Yougang Yao Linling Zou Chengdu Neusoft University Chengdu China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

In the era of data flood, information security and privacy protection issues have become increasingly prominent, especially in the field of data integration and sharing and cross-border applications. Traditional encryption and decryption technologies have been overwhelmed and faced with severe challenges of huge data volumes and complex scenarios. This paper innovatively conceives an information security and privacy protection framework that integrates federated learning algorithms. Its core purpose is to accurately resolve the dual dilemma of information leakage and privacy erosion under the background of big data. Relying on the sophisticated mechanism of federated learning, this architecture builds a solid line of defense for user privacy in multi-party data collaboration scenarios, and simultaneously improves data computing performance and security. The system construction covers distributed data management components, privacy protection solutions, and deep learning-driven security evaluation algorithms. The three complement each other and jointly build a comprehensive security protection network. By using a series of actual big data sets to implement simulation experiments, the verification results show that this model significantly improves data processing accuracy under the premise of privacy protection, and shows excellent superiority in dimensions such as information security, privacy protection, and computing performance. Simulation data analysis further confirmed that the model surpasses traditional algorithms in key measurement indicators such as privacy exposure risk, computing cost, and model accuracy, demonstrating its broad application potential.

关键词： data privacy Accuracy Federated learning Computational modeling Distributed databases Collaboration Big data Information leakage data models Protection

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MIND: A Privacy-Preserving Model Inference Framework via End-Cloud Collaboration

MIND: A Privacy-Preserving Model Inference Framework via End...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Siyuan Guan Ziheng Hu Guotao Xu Yao Zhu Bowen Zhao Guangzhou Institute of Technology Xidian University Guangzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Image processing and analysis capabilities have been revolutionized by the rapid advancement of deep learning technologies. Model inference enables powerful image analytics, however, complex neural networks often require more computing and storage resources than end device can provide. While cloud-based model inference helps alleviate the resource constraints of these devices. However, transferring sensitive image data to the cloud server raises significant privacy concerns. Existing approaches usually either encrypt the images locally and send them to the cloud server or deploy the model locally, which fails to exploit the computing capability of the end device or reveals a machine-learning model. To this end, in this paper, we proposed MIND, a privacy-preserving model inference framework via end-cloud collaboration. MIND makes the end device and the cloud server jointly perform layer-by-layer computations required by model inference through cryptographic primitives including the homomorphic cryptosystem and additive secret sharing. For linear layers, MIND always chooses the less expensive option between data encryption and model parameter encryption. Results of experimental evaluations on several models show MIND is effective and outperforms the existing solutions.

关键词： Performance evaluation Privacy Additives Runtime Computational modeling Neural networks Collaboration data models Servers Protection

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Machine Learning-Based Power Allocation Optimization Algorithm for Enhanced CR-NOMA Network

Machine Learning-Based Power Allocation Optimization Algorit...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yu Fu Bingcai Chen Qian Ning Kai Lin College of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science and Technology Xinjiang Normal University Urumq China College of Electronics and Information Engineering Sichuan University Chengdu China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Hybrid non-orthogonal multiple access (NOMA) is a promising scheme to significantly improve communication performance within a 5G mobile network. In this article, a NOMA network is established in the context of underlay cognitive radio (CR) firstly. In addition, the combination of artificial noise (AN) and decode-and-forward (DF) technology is applied at the unmanned aerial vehicle (UAV) relay. We then derive the secrecy outage probability (SOP), which is affected by power allocation factors, to measure the system secrecy performance. Finally, a machine learning (ML) power allocation optimization algorithm is proposed considering both network security and user fairness. To be specific, we employ a revised decision tree (DT) embedded multilayer perceptron (MLP) in the leaf nodes, aiming to utilize the strengths of both ML algorithms effectively and find the optimal groups of systems with various parameters rapidly. The data results demonstrate that the introduced algorithm provides satisfactory predictions and obtains more accurate coefficients compared to other ML algorithms.

关键词： NOMA Machine learning algorithms Machine learning Probability Prediction algorithms Autonomous aerial vehicles Power system reliability Resource management cognitive radio Optimization

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